Forecasting techniques are very crucial in management of private transportation vehicles in metropolis where supply largely mismatches the demand at peak hours. Taxi or Cab operators struggle to optimize their service by reducing concentration of umpteen unused cabs at and around one area and evenly dispersing them to pull in more bookings. Looking through a customer's lens it points towards a more fundamental problem that the cab operators usually provide service only on real-time basis. This problem restricts customer from booking a ride beforehand and also cab operators to predict availability of a cab on a future date and time from getting a prospective client.
Existing prior art enables a prospective customer to call/text/email an operator with his location, destination & time. The operator in turn confirms it by receiving acceptance from one or more available drivers in vicinity for a particular time slot on the same day.
US patent US 2011/0099040 A1, titled “A mobile taxi dispatch system” describes/discloses a system which receives a request for a taxi/cab from a user. The system, then, selects one or more taxi drivers available and accepts their confirmation. It selects one or more taxi from those which accepted the request.
The whole selection process is based on certain measures. It sends confirmation details to the user and the selected taxi.
US patent US 2010/0185486 A1, titled “Determining the demand associated with origin-destination pairs for bus ridership forecasting” describes a method to determine the demand for a pair of stops, termed as origin-destination pairs. A count data of passengers getting on and off at each stop is received by the computer system. It also includes the operation of a translation module to deduce the demand based on the counts at each stop.
US patent US 2013/0144831 A1, titled “Predicting Taxi Utilization Information” discloses a system in which techniques are described for automatically analyzing possible events in information predicting taxi demand. It generates a representative taxi demand in future. The contingent demand information is generated for, like projecting expected likelihood of finding a passenger at certain place or time. The representative contingent taxi demand information is generated through historical demand data collected through data sensors in or near roads or through publicly available data sets.
However, the methods discussed above for optimization of taxi/cab services do not utilize historical data to analyse load balancing with a mechanism to display the same in real-time for forward reservation of cab as well as ‘borrowing’ from vicinity in case of exigency.